Zenglin Xu

Zenglin Xu

Resume(个人简介

Zenglin Xu, Professor and PhD Advisor
University of Electronic Science & Technology of China, director of the statistical machine intelligence and learning laboratory,
University of Electronic Science and Technology of China.
Professor Xu Zenglin’s main research interests are machine learning and its application in social network analysis, Internet, computational biology, information security and so on. He published more than 80 papers in top conferences and journals, includes ICML, IJCAI, AAAI, IEEE TPAMI, IEEE TNNLS, cited nearly 1500 times, published 2 monographs, and in 2012 in Toronto, the International Conference on artificial intelligence (AAAI) to do the teaching report. He made a teaching report on the International Conference on artificial intelligence (AAAI) in Toronto in 2012. Professor Xu Zenglin is reviewer of machine learning and artificial intelligence fields of the main journals, including IEEE, TPAMI JMLR and so on. He served as the fund reviewer of the Hongkong Education Funding Council. He has repeatedly served as the committee members of a major international conference in the field of artificial intelligence, such as AAAI/IJCAI and other meetings; repeatedly served as chairman of the Organizing Committee of the Symposium on machine learning and big data research.Professor Xu Zenglin is one of the instructors of Award for Best Student Paper Award in the AAAI2015 which is the top international conference in the Field of Artificial Intelligence and the 2016 Asian Machine Learning Conference (ACML) Best Student Paper Award. Professor Xu Zenglin Professor Xu Zenglin received the Young Researcher Award of the Asia-Pacific Neural Network Association (APNNS) in September 2016.

Professor Xu Zenglin graduated from Chinese University Hong Kong in 2009 with a major in computer science and Engineering, under the guidance of Professor Irwin king, vice president of the Academy of engineering of the Chinese University of Hong Kong, Asia Pacific neural network association APPNA executive vice president and professor Michael R. Lyu, IEEE fellow, American Association for the advancement of Science (AAAs fellow). He has visited and engaged in academic research work in Michigan State University, Information Research Institute of Planck Marx of Germany and Saarland University, Purdue University and other famous research institutions; the major collaborators include professor Jin Rong of the University of Michigan State University, Professor Oja Erkki, Academician of Finland Academy of Sciences/IEEE Fellow, professor Qi Ninghui of Purdue University, Professor Li Alan of Purdue University, etc..

Major academic achievement and academic contribution

a) Books or Edited Special Issues

  1. Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2018 (expected).
  2. Yi Fang, Zenglin Xu, Jiang Bian, and Ziad Al Bawab. International Journal of Web Science, Special Issue on Social Web Search and Mining. Inderscience, 2013.
  3. Zenglin Xu, Irwin King, and Michael R. Lyu. More Than Semi-supervised Learning: A Unified View on Learning with Labeled and Unlabeled Data. LAP LAMBERT Academic Publishing, 2010.

b) Invited Book Chapters

  1. Zenglin Xu, Mingzhen Mo, and Irwin King. Computational intelligence. In Alexandru Floares, editor, Semi-supervised Learning, pages 1-16. Nova Science Publishers, 2012.
  2. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Zhangbin Zhou. A novel discriminative naive bayesian network for classification. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Applications and Graphical Models, pages 1-12. IDEA Group Inc., New York, 2007.

c) Refereed Journal Articles

  1. Liyang Hao, Siqi Liang, Jinmian Ye and Zenglin Xu. TensorD: A tensor decomposition library in TensorFlow Neurocomputing, 318, 196-200, 2018.
  2. Zhao Kang, Liangjian Wen, Wenyu Chen and Zenglin Xu. Low-rank kernel learning for graph-based clustering Knowledge-Based Systems, 2018. DOI: doi.org/10.1016/j.knosys.2018.09.009.
  3. Zenglin Xu, Bin Liu, Shandian Zhe, Haoli Bai, Zihan Wang and Jennifer Neville. Variational Random Function Model for Network Modeling IEEE transactions on neural networks and learning systems, 99, 1-7, 2018.
  4. Shudong Huang, Peng Zhao, Yazhou Ren, Tianrui Li, and Zenglin Xu. Self-paced and soft-weighted nonnegative matrix factorization for data representation, Knowledge-based System, 2018. DOI:10.1016/j.knosys.2018.10.003
  5. Shudong Huang, Yazhou Ren, and Zenglin Xu. Robust Multi-View Data Clustering with Multi-View Capped-Norm K-means, Neurocomputing, 2018.
  6. Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm, Knowledge-Based Systems, 2018.
  7. Lirong He, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, Zenglin Xu. Knowledge Base Completion by Variational Bayesian Neural Tensor Decomposition. Cognitive Computation, 2018
  8. Bin Liu, Yingming Li, Zenglin Xu, Manifold regularized matrix completion for multi-label learning with ADMM. Neural Networks, https://doi.org/10.1016/j.neunet.2018.01.011, 2018
  9. Shudong Huang, Zenglin Xu, Jiancheng Lv, Adaptive Local Structure Learning for Document Co-clustering, Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2018.02.020, 2018
  10. Shudong Huang, Hongjun Wang, Tao Li, Tianrui Li, and Zenglin Xu. Robust Graph Regularized Nonnegative Matrix Factorization for Clustering, Data Mining and Knowledge Discovery, 32(2): 483-503 (2018).
  11. Zenglin Xu, Shandian Zhe,Yuan(Alan) Qi and Peng Yu. Association Discovery and Diagnosis of Alzheimer’s Disease with Bayesian Multiview Learning. Journal of Artificial Intelligence Research, 56 (2016).
  12. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Budget constrained non-monotonic feature selection. Neural Networks, 71, 214-224, 2015
  13. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric models for multiway data analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.37, no.2, pp.475-487, 2015.
  14. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. Efficient sparse generalized multiple kernel learning. IEEE Transactions on Neural Networks, 22(3):433-446, 2011.
  15. Zenglin Xu, Irwin King, Michael R. Lyu, and Rong Jin. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7):1033-1047, 2010.
  16. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, and Michael R. Lyu. A novel kernel-based maximum a posteriori classification method. Neural Networks, 22(7):977-987, 2009.
  17. Zenglin Xu, Irwin King, and Michael R. Lyu. Feature selection based on minimum error minimax probability machine. International Journal of Pattern Recognition and Artificial Intelligence, 21(8):1-14, 2007.

d) Refereed International Conference Articles

  1. Yazhou Ren, Xiaohui Hu, Ke Shi, Guoxian Yu, Dezhong Yao, and Zenglin Xu. Semi-supervised DenPeak Clustering with Pairwise Constraints Pacific Rim International Conference on Artificial Intelligence, 2018
  2. Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin Xu: Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification, IJCAI, 2018.
  3. Hao Liu, Lirong He, Haoli Bai, Kun Bai, Zenglin Xu: Structured Inference for Recurrent Hidden Semi-Markov Model, IJCAI, 2018.
  4. Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu: Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition. CVPR, 2018.
  5. Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska: SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks, PPoPP, 2018.
  6. Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu: Unified Spectral Clustering with Optimal Graph. AAAI, 2018
  7. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Incomplete Labels. AAAI, 2018
  8. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. AAAI 2018
  9. Xiaofan Que, Yazhou Ren, Jiayu Zhou, Zenglin Xu: Regularized Multi-source Matrix Factorization for Diagnosis of Alzheimer’s Disease. ICONIP(1) 2017: 463-473
  10. Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, Zenglin Xu: Robust Softmax Regression for Multi-class Classification with Self-Paced Learning. IJCAI 2017: 2641-2647
  11. Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael R. Lyu: Simple and efficient parallelization for probabilistic temporal tensor factorization. IJCNN 2017: 1-8
  12. Shudong Huang, Zenglin Xu, Fei Wang: Nonnegative matrix factorization with adaptive neighbors. IJCNN 2017: 486-493
  13. Liqiang Wang, Yafang Wang, Bin Liu, Lirong He, Shijun Liu, Gerard de Melo, Zenglin Xu: Link prediction by exploiting network formation games in exchangeable graphs. IJCNN 2017: 619-626
  14. Yazhou Ren, Peng Zhao, Zenglin Xu, Dezhong Yao: Balanced self-paced learning with feature corruption. IJCNN 2017: 2064-2071
  15. Bin Liu, Zenglin Xu, Bo Dai, Haoli Bai, Xianghong Fang, Yazhou Ren, Shandian Zhe: Learning from semantically dependent multi-tasks. IJCNN 2017: 3498-3505
  16. Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang,Zenglin Xu: Efficient Communications in Training Large Scale Neural Networks. ACM Multimedia (Thematic Workshops) 2017: 110-116
  17. Hao Liu, Haoli Bai, Lirong He, Zenglin Xu: Stochastic Sequential Neural Networks with Structured Inference. CoRR abs/1705.08695 (2017)
  18. Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang: Learning with Feature Network and Label Network Simultaneously. In AAAI’17: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 1410-1416
  19. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Multi-view Learning with Limited and Noisy Tagging. In IJCAI’16: Proceedings of the 25th International Joint Conference on Artificial Intelligence.
  20. Shandian Zhe, Yuan Qi, Youngja Park, Zenglin Xu, Ian Molloy, and Suresh Chari DinTucker: Scaling up Gaussian Process Models on Large Multidimensional Arrays . In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
  21. Yingming Li, Ming Yang, Zenglin Xu, and Zhongfei (Mark) Zhang Learning with Marginalized Corrupted Features and Labels Together. In AAAI’16: Proceedings of the 26th AAAI Conference on Artificial Intelligence.
  22. Shandian Zhe, Zenglin Xu, Xinqi Chu, Yuan Qi and Yongja Park Scalable Nonparametric Multiway Data Analysis. In AISTATS’15: Proceedings of the 18th Proceedings of International Conference on Artificial Intelligence and Statistics. 2015. (AR: 127/442= 28.7%)
  23. Shandian Zhe, Zenglin Xu, and Yuan Qi. Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease. In AAAI’15: Proceedings of the 25th AAAI Conference on Artificial Intelligence. Outstanding student paper honorable mention, 2015. (AR: 531/1991= 26.7%)
  24. Zenglin Xu, Rong Jin, Bin Shen and Shenghuo Zhu. Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation In AAAI’15: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2015. (AR: 531/1991= 26.7%)
  25. Christopher Gates, Ninghui Li, Zenglin Xu, Suresh N. Chari, Ian Molloy, and Youngja Park. Detecting Insider Information Theft Using Features from File Access Logs. European Symposium on Research in Computer Security (ESORICS), 2014.
  26. Bin Shen, Zenglin Xu and Jan P. Allebach. Kernel Tapering: a Simple and Effective Approach to Sparse Kernels for Image Processing. International Conference on Image Processing, 2014.
  27. Shandian Zhe, Zenglin Xu and Yuan (Alan) Qi. Joint association discovery and diagnosis of Alzheimer’s disease by supervised heterogeneous multiview learning. Pacific Symposium on Biocomputing, 2014.
  28. Shouyuan Chen, Irwin King, Michael R. Lyu, and Zenglin Xu. Recovering pairwise interaction tensor. Neural Information Processing Systems (NIPS), 2013. (AR: 360/1420= 25.3%, Spotlight: 52/1420 = 3.7%)
  29. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Infinite tucker decomposition: Nonparametric bayesian models for multiway data analysis. In ICML’12: Proceedings of the 29th International Conference on Machine Learning, pages 1023-1030, New York, NY, USA, 2012. Omnipress. (AR: 243/890 = 27.3%)
  30. Feng Yan, Zenglin Xu, and Yuan (Alan) Qi. Sparse matrix-variate gaussian process blockmodels for network modeling. In UAI’11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence,
    pages 745-752. AUAI Press, 2011. (AR: 96/285=33.6%)
  31. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Sparse matrix-variate t process blockmodels. In AAAI ‘11: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
  32. Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu, and Irwin King. Smooth optimization for effective multiple kernel learning. In AAAI ‘10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 2010. (AR: 264/982=26.9%)
  33. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simple and efficient multiple kernel learning by group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1175-1182. Omnipress, 2010. (AR: 152/594=25.6%)
  34. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online learning for group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1191-1198. Omnipress, 2010. (AR: 152/594=25.6%)
  35. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu. Robust metric learning by smooth optimization. In UAI ‘10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pages 244-251. AUAI Press, 2010. (AR: 88/260=33.8%)
  36. Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semi-supervised feature selection via manifold regularization. In IJCAI’09: Proceedings of the 21th International Joint Conference on Artificial Intelligence, pages 1303-1308, 2009.
  37. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, and Zhirong Yang. Adaptive regularization for transductive support vector machine. In Y. Bengio, L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2125-2133. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 = 7.8%)
  38. Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2169-2177. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 = 7.8%)
  39. Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Non-monotonic feature selection. In ICML ‘09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 1145-1152, New York, NY, USA, 2009. ACM. (160/640 = 25%)
  40. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell. Supervised self-taught learning: Actively transferring knowledge from unlabeled data. In IJCNN ‘09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
  41. Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient multiple kernel learning. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS), pages 1825-1832. 2008. (AR: 250/1022 = 24%)
  42. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R. Lyu. Semi-supervised text categorization by active search. In CIKM ‘08: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 1517-1518, New York, NY, USA, 2008. ACM Press. (AR: 256/772 = 33%)
  43. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi-supervised learning from general unlabeled data. In ICDM ‘08: Proceedings of IEEE International Conference on Data Mining, pages 273-282, Los Alamitos, CA, USA, 2008. IEEE Computer Society. (AR: 70/724 = 9%)
  44. Jianke Zhu, Steven C. Hoi, Zenglin Xu, and Michael R. Lyu. An effective approach to 3d deformable surface tracking. In ECCV’08: Proceedings of the 10th European Conference on Computer Vision, pages 766-779, Berlin,
    Heidelberg, 2008. Springer-Verlag.
  45. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Efficient convex relaxation for transductive support vector machine. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1641-1648. MIT Press, Cambridge, MA, 2007. (217/975 = 22%)
  46. Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classification with heterogeneous data fusion. In WWW ‘07: Proceedings of the 16th International Conference on World Wide Web, pages 1171-1172, New York, NY, USA, 2007. ACM Press.

Research project

  1. Teachers basic research projects of Central University, Multi - source data analysis and its application in emergency decision-making of surgical emergency ward, forty thousands, 2017/1-2018/12.
  2. Major key cultivation projects in central university, Research and application of knowledge calculation based on social network big data, 1 million, 2017/1-2019/12.
  3. UESTC 985 matching funds, research on Key Technologies of advanced machine learning platform, 2 million, 2014-2018.
  4. National Thousand Youth Talents Plan of start-up funding, research on Key Technologies of advanced machine learning platform, 2 million, 2015-2017。
  5. Key Laboratory of network data science and technology, Chinese Academy of Sciences, Scalable Bayesian learning algorithm and its application in large scale social network, host, 60 thousand yuan, 2015-2016.
  6. Basic research funding of the Central University, bayesian learning algorithm based on matrix distribution and its application in the analysis of social network, 100 thousand yuan, 2015-2016.
  7. National Natural Science Foundation of China emergency management project, based on matrix distribution of statistical machine learning algorithm of professional athletes complex social network construction and application research, host, 200 thousand yuan, 2015/1-2015/12,.
  8. The National Natural Science Foundation of China, the non parametric Bayesian learning technology in the large-scale tensor analysis, 2016/1-2019/12,730 thousand.